Trend dynamic sensor imaging using contour maps to visualize multiple trend data sets in a single view

ABSTRACT

A method and system for trend dynamic sensor imaging is described herein. In one embodiment a plurality of data sets is received and the plurality of data sets is pre-processed to transform the data of the plurality data sets to have a common scale. The pre-processed plurality of data sets is displayed by plotting the transformed data of the plurality of data sets on a grid according to a color gradient.

RELATED APPLICATION

This patent application claims the benefit under 35 U.S.C. §119(e) ofU.S. Provisional Application No. 61/723,265 filed Nov. 6, 2012, which isherein incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to data visualization, and, moreparticularly, to data visualization for analyzing physical processes.

BACKGROUND OF THE INVENTION

Data visualization is a technique used to transform large quantities ofdata into a manageable and useful form. Data visualization can beparticularly useful for analyzing a physical process, such assemiconductor manufacturing, where sensors provide more data than anindividual user can efficiently mentally process by simply reviewing theraw numerical data, by aiding a user to rapidly review sensor data sothat decisions regarding the physical process can be made effectively.

In some applications, a user would benefit from being able to use datavisualization to review data from a number of different data sourcesover a period of time, such that the user would be able to discern datatrends over time.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrates an embodiment of a trend dynamic sensor imaging (DSI)system.

FIG. 2 illustrates one embodiment of a method for trend DSI.

FIG. 3 illustrates another embodiment of a method for trend DSI.

FIG. 4 illustrates an exemplary screenshot of trend DSI in accordancewith one embodiment of the invention.

FIG. 5 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 6 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 7 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 8 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 9 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 10 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 11 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 12 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 13 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 14 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 15 illustrates an exemplary screenshot of trend DSI in accordancewith another embodiment of the invention.

FIG. 16 illustrates an exemplary computer system.

PETITION FOR COLOR DRAWINGS

The file of this patent contains at least one drawing/photographexecuted in color. Copies of this patent with colordrawing(s)/photograph(s) will be provided by the Office upon request andpayment of the necessary fee.

DETAILED DESCRIPTION

Embodiments of the present invention are directed to a method and systemfor visualizing data obtained from a physical process using trenddynamic sensor imaging (DSI). A trend DSI system receives a data setfrom each of a plurality of data sources for a time frame of interest,such as sensor data from a semiconductor manufacturing system, where thedata sources may provide data of different scales. The trend DSI systemthen pre-processes the data in the data sets such that the data has acommon scale. Once the data has been pre-processed, the system displaysthe data sets on a grid by plotting the transformed data according to acolor gradient or scale. Here, the user is able to efficiently andeffectively consider large quantities of data for detection of anomalies(e.g., outliers), such as flaws in a semiconductor manufacturingprocess, and how changing one parameter affects other parameters. Forexample, a user is able to view multiple SPC trend charts in a singleview, and expose and/or investigate features of interest.

FIG. 1 illustrates an example network architecture 100 in whichembodiments of the present disclosure can operate. In one embodiment,network architecture 100 includes a trend DSI system 102 connected tophysical process system 104 and a plurality of data sources 106 (e.g.,via a network such as a local area network or Internet). The system 102can be hosted by any type of computing device including desktopcomputers, laptop computers, handheld computers or similar computingdevices.

The physical process system 104 can include different types of physicalprocesses from which data can be generated. For example, the physicalprocess system 104 could include manufacturing tools. Examples ofmanufacturing tools include semiconductor manufacturing tools, such asetchers, chemical vapor deposition furnaces, etc, for the manufacture ofelectronic devices. Manufacturing such devices includes dozens ofmanufacturing steps involving different types of manufacturingprocesses, which may be known as a recipe. In another example, thephysical process system 104 could include a biomedical application,where physical processes cause parameters such as temperature andresistance to vary over time. The physical process system 104 caninclude any type of computing device, including desktop computers,laptop computers, handheld computers or similar computing devices, tocontrol the system.

In other embodiments, the physical process system 104 is not connectedto the system 102.

The architecture 100 may also include data sources 106 that generatedata about the physical process system 104 over time. For example, thedata sources 106 can be sensors that detect information about thephysical process system 104. In the example of a semiconductormanufacturing system, hundreds of sensors may detect data pertaining tomanufacturing tools, where the data may include, for example,temperature, pressure, gas flow, and RF power, among others, over time.Data sources 112 may be part of the physical process system 104 or maybe connected to the physical process system 104 (e.g., via a network).

Each data source 106 generates a data set 110 including the data fromthe data source 106. A data collector 108 of system 102 can collect thedata sets 110 from the data sources 106 and store them in a persistentstorage unit 112.

The system 102 also includes a pre-processing tool 114 for transformingdata of the data sets 110 to have a common scale. Since the retrieveddata sets 110 all have different scales, the data of the data sets 110is transformed to a common scale so that the data sets 110 can bemeaningfully visualized in one display. For example, one of the datasets 110 may have a larger range of values (or scale) than another ofthe data sets 110. Here, if a user were to visualize the raw data ofthese data sets 110 simply plotted on a grid, the user would not be ableto appreciate relative variations of the data in all of the data sets110. In other words, the user would not be able to perceive variationsin the data set 110 with the smaller range of values (or scale) whenplotted on a grid scaled to accommodate the data set 110 with the largerrange of values (or scale). Therefore, the data of the data sets 110must be transformed to have a common scale in order to be effectivelyperceived together on one display.

In one embodiment, the system 102 includes a graphical user interface(GUI) generator 116 that generates a GUI to display the data sets 110.In particular, the GUI generator 116 displays the data sets 110 byplotting the transformed data according to a color gradient. The GUIgenerator 116 can generate GUIs to illustrate data trends for a largenumber of data sets over time. FIGS. 4-12, described in greater detailbelow, illustrate exemplary GUIs for enabling a trend DSI system 102.

The GUIs generated by the GUI generator 116 can be accessible via abrowser on one or more client machines 118, or, in one embodiment, theGUI generator can reside on the client machine 118 and can receive thetransformed data from the pre-processing tool 114 via a network. Clientmachines 118 can be any type of computing device including desktopcomputers, laptop computers, mobile communications devices, cell phone,smart phones, handheld computers or similar computing devices.

In one embodiment, the physical process system 104, the data sources106, the persistent storage unit 112, and the client machine 118 areconnected to the trend DSI system 102, which make a direct connection oran indirect connection via a hardware interface (not shown), or via anetwork (not shown). The network can be a local area network (LAN), suchas an intranet within a company, a wireless network, a mobilecommunications network, or a wide area network (WAN), such as theInternet or similar communication system. The network can include anynumber of networking and computing devices such as wired and wirelessdevices.

The division of functionality presented above is by way of example only.In other embodiments, the functionality described could be combined intoa monolithic component or sub-divided into any combination ofcomponents. The data collector 108, the GUI generator 116, the clientmachine 118, and the pre-processing tool 114 can be hosted on a singlecomputer system, on separate computer systems, or on a combinationthereof.

FIG. 2 illustrates one embodiment of a method 200 for trend DSI. Method200 can be performed by processing logic that can comprise hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device), or acombination thereof. In one embodiment, method 200 is performed by thetrend DSI system of FIG. 1.

At block 202, processing logic of the system 102 receives the data set110 for each data source 106. The trend DSI system 102 can receive thedata set 110 from the data sources 106, from the persistent storage unit112, or via some other source.

At block 204, processing logic of the pre-processing tool 114 of thesystem 102 pre-processes the data sets 110 to transform the data to havea common scale.

At block 206, processing logic displays the pre-processed data sets byplotting the transformed data on a grid according to a color gradient.In an embodiment, the transformed data is displayed on an XYZ grid,where the X-axis is the sample number, the Y-axis is the data set, and Zis the transformed value of the data plotted as a color of the colorgradient. For example, for embodiments used in semiconductormanufacturing, X is the sample number, Y is the sensor, and Z is thetransformed value of the sensor. A color gradient is used to display theZ values on the grid. In one embodiment, the transformed data isassociated with the color gradient via a look-up table.

In one embodiment, X is a time-based value. However, X is not limited tothis, and X may be a tool/chamber-based value, e.g., where the first setof data is from chamber A, the second from chamber B, etc.

For example, the color gradient may be a −9 to +9 linear color gradientfor displaying the Z values on the grid. FIG. 4 illustrates an exemplaryscreenshot of trend DSI in accordance with another embodiment of theinvention, where a −9 to +9 linear color gradient is used for displayingthe Z values on the grid.

In block 208, in one embodiment, processing logic receives an optionallyuser-selected data point of interest. Here, the user may select a datapoint of interest from the displayed GUI. For example, the user may usea cursor control device (e.g., a mouse) to select a data point ofinterest (e.g., by right clicking on the data point), such as ananomaly, in the displayed data.

In block 210, in the embodiment where the user may select a data point,processing logic displays additional data associated with the selecteddata point. For example, a pop-up may be displayed with the data setname, the Z value, and/or a link to an SPC chart for that data set. FIG.5 illustrates an exemplary screenshot of trend DSI in accordance with anembodiment where a user may select a data point and additionalinformation about the data point may be displayed. In one embodiment,the user is able to exclude a selected data source from the grid.

FIG. 3 illustrates a method of pre-processing data for trend DSIaccording to an embodiment of the present invention. Method 300 can beperformed by processing logic that can comprise hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device), or acombination thereof. In one embodiment, method 300 is performed by thenetwork architecture 100 of FIG. 1. In particular, the pre-processingtool 114 of FIG. 1 performs the method 300.

In block 302, according to one embodiment, processing logic of thepre-processing tool 114 standardizes the data set 110. In one embodimentof the transformation of the data, the data is standardized using thefollowing formula:

$\mspace{79mu} {z_{i} = \frac{x_{i} - \overset{\_}{x}}{\sigma}}$${where}\mspace{14mu} \overset{\_}{x}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {mean}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {sensor}\mspace{14mu} {and}\mspace{14mu} \sigma \mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {StdDev}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {sensor}$

After standardization, the majority of the data should fall in the +/−3sigma range.

A user can select a method by which the mean and standard deviation usedfor standardizing the data are determined. The user may select to useall of the data retrieved to determine the mean and standard deviation,which may be a default method. The user may instead select to use thefirst n-samples to determine the mean and standard deviation, whichallows the user to see how the data drifts over time. The user mayinstead select to use summary statistics from a selected time period,such as a day, which allows the users to see how the data driftscompared to a selected time period.

Other types of data transformation can be used to handle different datatypes and to expose different features. For example, the data may betransformed into a 0-1 range, which is useful for analyzing real time(or raw data) as compared to statistical (or SPC data) where a −9 to +9range may be more useful.

In block 304, processing logic of the pre-processing tool 114 excludesdata sets 110 having a standard deviation of zero, since these data sets110 are likely not of interest.

In block 306, in one embodiment, processing logic may apply anoptionally user-selected data window to the data of the data sets 110.For example, various windows (ex., Hamming or Hanning windows) could beapplied to the transformed data to expose different features in the datasets 110.

In block 308, in one embodiment, processing logic may apply anoptionally user-selected data range to the data sets 110 so that datathat is not of interest is not displayed. In one embodiment, only Zvalues greater than a selected threshold are shown to help visuallyisolate features of interest. For example, the threshold values may be0, 3 and 6. In one embodiment, any Z value greater than a positivethreshold or less than a negative threshold is plotted as a 0. FIG. 6illustrates an exemplary screenshot of trend DSI in accordance with anembodiment where there is a threshold of 3.

In block 310, in one embodiment, processing logic may apply anoptionally user-selected color scheme for display of the standardizeddata sets. In one embodiment, different color palettes can be selectedto highlight or expose different features of interest. For example, blueto white to red may be particularly effective at showing trend data. Inanother embodiment, the user may select a desired scale for the colorpallets. For example, a linear scale may be a default scale. However,log scaling, polynomial scaling or another user defined scaling may beused to expose different features. In one embodiment, a direction of ashift or trend of the data is not of interest, and the user is onlyinterested in detecting the magnitude of the shift or trend. Here,absolute Z (the absolute value of Z) is plotted to improvevisualization. In this embodiment, the display range is 0 to 9. FIG. 7illustrates an exemplary screenshot of trend DSI in accordance with anembodiment where absolute Z is plotted. In one embodiment, absolute Z isthe default selection.

In some instances, interpolation between data sets (e.g., sensors) makesinterpreting the data more difficult. Therefore, in one embodiment, adummy data set with zero values is interleaved between each real dataset. FIG. 8 illustrates an exemplary screenshot of trend DSI inaccordance with an embodiment where zero values are interleaved betweenreal data sets. In one embodiment, sensor interleaving is not a defaultselection.

In one embodiment, the user has the ability to zoom into an area ofinterest on the display. In another embodiment, the user is able toscroll left or right (along the X values) and up or down (along the datasets) to search for an area of interest, including when the user haszoomed into an area of the display. In yet another embodiment, the usercan then select a data point (e.g., by right clicking on a point), andadditional data about the data point may be displayed.

FIG. 9 illustrates an exemplary screenshot of trend DSI where the userselected to use all retrieved data for reference, has zoomed into anarea of interest, has selected to plot absolute Z, has not selected tointerleave zero values between data sets, and has set a threshold ofzero.

FIG. 10 illustrates an exemplary screenshot of trend DSI where the userselected to use the first 50 runs for reference, has zoomed into an areaof interest, has selected to plot absolute Z, has not selected tointerleave zero values between data sets, and has set a threshold ofzero.

FIG. 11 illustrates an exemplary screenshot of trend DSI where the userselected to use the first 50 runs for reference, has zoomed into an areaof interest, has selected to plot absolute Z, has selected to interleavezero values between data sets, and has set a threshold of three.

In another embodiment of the present invention, a user may select to usethe median value of the data instead of the mean value of the data inthe transformation of the data. For example, if the data set containsmulti-modal switches, the mean would average out the modality, whereasmedian will find the most common mode and reference the data against themost common mode. In this embodiment, the data is standardized using thefollowing formula:

$\mspace{79mu} {z_{i} = \frac{x_{i} - \overset{\_}{x}}{\sigma}}$${where}\mspace{14mu} \overset{\_}{x}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {median}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {sensor}\mspace{14mu} {and}\mspace{14mu} \sigma \mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {StdDev}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {sensor}$

In one embodiment, the default selection should be to use the mean valueof the data.

In one embodiment, the default color palette for the values is blue (forlow data values) to white to red (high for high data values). This colorscheme helps the user visualize the actual changes in the data. FIG. 12illustrates an exemplary screenshot according to this color palette. Inthis embodiment, the default may be to not plot Absolute Z.

In one embodiment, at least some of the data source names (e.g., sensornames) are shown on the Y-axis.

In another embodiment, previous similar plots are subtracted from thecurrent plot of interest so that differences between the plots becomemore apparent.

In an embodiment, different color palettes may be selected depending onthe data sets and/or the features to be visualized or extracted. Here, auser would first select the color palette to use (e.g., red to white toblue, blue to red, etc.), and then select the transformation on thecolor palette.

Below are some examples of transformations on the color palette. Inthese the same color palette is used, but different transformations showdifferent features. In one embodiment, a user could select multipletransformations depending on the features to be displayed.

FIG. 13 illustrates an exemplary screenshot of trend DSI where the userselected a color palette transform that is linear and ranges from −9 to9, which may be a default selection in an embodiment.

FIG. 14 illustrates an exemplary screenshot of trend DSI showing atransform lookup where the user selected to view only Z-values less than−6 and greater than +6. Here, the following transform could be used:

If abs(z)<6 then z=0 else z=z

FIG. 15 illustrates an exemplary screenshot of trend DSI showing atransform lookup where the user selected to amplify variations in theextremities of the data. Here, the following tangent based transformcould be used:

z← tan(z*.16)

FIG. 16 is a block diagram illustrating an exemplary computing device(or system) 1600. The computing device 1600 includes a set ofinstructions for causing the computing device 1600 to perform any one ormore of the methodologies discussed herein. The machine may operate inthe capacity of a server machine in client-server network environment.The machine may be a personal computer (PC), a set-top box (STB), aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlecomputing device is illustrated, the term “computing device” shall alsobe taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The exemplary computer device 1600 includes a processing system(processing device) 1602, a main memory 1604 (e.g., read-only memory(ROM), flash memory, dynamic random access memory (DRAM) such assynchronous DRAM (SDRAM), etc.), a static memory 1606 (e.g., flashmemory, static random access memory (SRAM), etc.), and a data storagedevice 1616, which communicate with each other via a bus 1608.

Processing device 1602 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1602 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1302 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), a digital signalprocessor (DSP), network processor, or the like. The processing device1602 is configured to execute the Trend DSI system 102 of FIG. 1 forperforming the operations and steps discussed herein.

The computing device 1600 may further include a network interface device1622. The computing device 1600 also may include a video display unit1610 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)),an alphanumeric input device 1612 (e.g., a keyboard), a cursor controldevice 1614 (e.g., a mouse), and a signal generation device 1620 (e.g.,a speaker).

The data storage device 1616 may include a computer-readable storagemedium 1624 on which is stored one or more sets of instructions 1626embodying any one or more of the methodologies or functions describedherein. The instructions 1626 may also reside, completely or at leastpartially, within the main memory 1604 and/or within the processingdevice 1602 during execution thereof by the computing device 1600, themain memory 1604 and the processing device 1602 also constitutingcomputer-readable media. The instructions 1626 may further betransmitted or received over a network 1628 via the network interfacedevice 1622.

While the computer-readable storage medium 1624 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present invention.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

Some portions of the detailed description that follows are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a result.The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “determining”, “identifying”, “comparing”, “sending”, orthe like, refer to the actions and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Embodiments of the invention also relate to an system for performing theoperations herein. This system can be specially constructed for therequired purposes, or it can comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program can be stored in a computer (ormachine) readable storage medium, such as, but not limited to, any typeof disk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flashmemory, or any type of media suitable for storing electronicinstructions.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems can be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the method steps. The structure for a variety ofthese systems will appear from the description herein. In addition,embodiments of the present invention are not described with reference toany particular programming language. It will be appreciated that avariety of programming languages can be used to implement the teachingsof the invention as described herein.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the invention should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method comprising: receiving, by a processingdevice, a plurality of data sets; pre-processing, by the processingdevice, the plurality of data sets, wherein the pre-processing comprisestransforming data of the plurality data sets to have a common scale; anddisplaying, on a display, the pre-processed plurality of data sets byplotting the transformed data of the plurality of data sets on a gridaccording to a color gradient.
 2. The method of claim 1, wherein thedata of the plurality of data sets comprises sensor values, a first axisof the grid is a sample number, and a second axis of the grid is asensor.
 3. The method of claim 2 further comprising: receiving a userselection of a plotted data point; and displaying additional dataassociated with the selected data point.
 4. The method of claim 3,wherein the displaying additional data associated with the selected datapoint further comprises displaying a sensor name associated with theselected data point, a sensor value associated with the selected datapoint, and a link to an SPC chart associated with the selected datapoint.
 5. The method of claim 1, wherein the pre-processing furthercomprises standardizing the plurality of data sets such that a majorityof the data of the plurality of data sets is in a range of +/−3 sigma.6. The method of claim 1, wherein the pre-processing further comprisesexcluding data sets having a standard deviation of zero.
 7. The methodof claim 1, wherein the pre-processing further comprises applying awindow to the data of the plurality of data sets.
 8. A systemcomprising: a memory; and a processing device coupled to the memory to:receive, by a processor, a plurality of data sets; pre-process, by theprocessor, the plurality of data sets, wherein the pre-processingcomprises transforming data of the plurality data sets to have a commonscale; and display, on a display, the pre-processed plurality of datasets by plotting the transformed data of the plurality of data sets on agrid according to a color gradient.
 9. The system of claim 8, whereinthe data of the plurality of data sets comprises sensor values, a firstaxis of the grid is a sample number, and a second axis of the grid is asensor.
 10. The system of claim 9, wherein the processing device isfurther to: receive a user selection of a plotted data point; anddisplay additional data associated with the selected data point.
 11. Thesystem of claim 10, wherein to display additional data associated withthe selected data point, the processing device is further to display asensor name associated with the selected data point, a sensor valueassociated with the selected data point, and a link to an SPC chartassociated with the selected data point.
 12. The system of claim 8,wherein to pre-process the plurality of data sets, the processing deviceis further to standardize the plurality of data sets such that amajority of the data of the plurality of data sets is in a range of +/−3sigma.
 13. The system of claim 8, wherein to pre-process the pluralityof data sets, the processing device is further to exclude data setshaving a standard deviation of zero.
 14. The system of claim 8, whereinto pre-process the plurality of data sets, the processing device isfurther to apply a window to the data of the plurality of data sets. 15.A non-transitory computer readable storage medium comprisinginstructions that, when executed by a processing device, cause theprocessing device to perform operations comprising: receiving aplurality of data sets; pre-processing the plurality of data sets,wherein the pre-processing comprises transforming data of the pluralitydata sets to have a common scale; and displaying, on a display, thepre-processed plurality of data sets by plotting the transformed data ofthe plurality of data sets on a grid according to a color gradient. 16.The non-transitory computer readable storage medium of claim 15, whereinthe data of the plurality of data sets comprises sensor values, a firstaxis of the grid is a sample number, and a second axis of the grid is asensor.
 17. The non-transitory computer readable storage medium of claim16, wherein the operations further comprise: receiving a user selectionof a plotted data point; and displaying additional data associated withthe selected data point.
 18. The non-transitory computer readablestorage medium of claim 17, wherein the displaying additional dataassociated with the selected data point further comprises displaying asensor name associated with the selected data point, a sensor valueassociated with the selected data point, and a link to an SPC chartassociated with the selected data point.
 19. The non-transitory computerreadable storage medium of claim 15, wherein the pre-processing furthercomprises standardizing the plurality of data sets such that a majorityof the data of the plurality of data sets is in a range of +/−3 sigma.20. The non-transitory computer readable storage medium of claim 15,wherein the pre-processing further comprises excluding data sets havinga standard deviation of zero.